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conversation-patterns

conversation-patterns

github.com/owl-listenerDesign & UI191329 installsUpdated 20w ago
Repository

About conversation-patterns

A design skill for structuring human–AI conversations, from turn-taking and repair to shared understanding. It helps interaction designers choose dialogue patterns and document conversation flows and rules.

Key features

  1. 01Turn-taking rules
  2. 02Repair sequence design
  3. 03Grounding checkpoints
  4. 04Dialogue pattern selection
  5. 05Conversation design artefacts

Use cases

  1. 01Mapping turn sequences for a conversation
  2. 02Specifying responses to misunderstandings
  3. 03Planning confirmation and summary points

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/owl-listener/ai-design-skills --skill conversation-patterns

After installing, type/conversation-patternsin Claude Code to confirm it loaded.

Details

From the project README

Conversation Patterns

Conversation between humans and AI follows predictable structural patterns. Designing these deliberately — rather than leaving them to model defaults — is core interaction design work.

Turn-Taking Structure

Every human-AI conversation has a rhythm. The designer decides:

  • Turn length: Short exchanges (chatbot-style) vs. long-form (essay generation). Match turn length to task complexity.
  • Turn initiation: Who speaks first? Does the AI greet, or wait? Does it ask a clarifying question before acting?
  • Turn boundaries: How does the user signal "I'm done"? How does the AI signal "I need more"?

Repair Sequences

Conversations break down. Repair is how they recover:

  • Self-repair: The AI detects its own error and corrects ("Actually, let me revise that...")
  • Other-repair: The user corrects the AI ("No, I meant the other one")
  • Clarification requests: The AI asks for disambiguation before proceeding
  • Graceful misunderstanding: The AI acknowledges confusion without frustrating the user Design repair sequences explicitly. Don't rely on the model to improvise them.

Grounding

Grounding is how participants establish shared understanding:

  • Confirmation: "Just to confirm, you want me to..."
  • Summarisation: "So far we've covered X, Y, and Z"
  • Reference resolution: Handling pronouns, anaphora, and ambiguous references
  • Context anchoring: Reminding the user what the AI knows and doesn't know

Dialogue Structure Patterns

Common structural patterns for human-AI conversation:

  • Interview: AI asks questions, user answers, AI synthesises
  • Co-creation: Turn-by-turn collaborative building
  • Instruction-execution: User gives command, AI performs, user evaluates
  • Exploration: Open-ended back-and-forth to discover possibilities
  • Guided workflow: AI leads the user through a multi-step process Choose the pattern that matches the task. Don't default to instruction-execution for everything.

Design Artefacts

  • Conversation flow diagrams showing turn sequences
  • Repair protocol specifications
  • Grounding checkpoints mapped to conversation stages
  • Turn-taking rules per interaction context

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